"Peak 10 is a hybrid infrastructure provider across the nation. We are in the thick of things when it comes to hybrid IT," explained Michael Fuhrman, Chief Technology Officer at Peak 10, in this SYS-CON.tv With major technology companies and startups seriously embracing Cloud strategies, now is the perfect time to attend 21st Cloud Expo, October 31 - November 2, 2017, at the Santa Clara Convention Center, CA, and June 12-14, 2018, at the Javits Center in New York City, NY, and learn what is going on, contribute to the discussions, and ensure that your enterprise is on the right path to Digital Transformation. Every Global 2000 enterprise in the world is now integrating cloud computing in some form into its IT development and operations. Midsize and small businesses are also migrating to the cloud in increasing numbers.

From personal care products to groceries and movies on demand, cloud-based subscriptions are fulfilling the needs of consumers across an array of market sectors. Nowhere is this shift to subscription services more evident than in the technology sector. By adopting an Everything-as-a-Service (XaaS) delivery model, companies are able to tailor their computing environments to shape the experiences they want for customers as well as their workforce. While the need to customize computing and technology services is a major driver, there are a number of other factors fueling the growing global corporate demand for XaaS. By securing utility-based offerings on a per-seat, per-month model based on usage, companies have more control over costs and can free up capital and resources to pursue new product and services initiatives.

He spent 13 years in the software industry working in consulting engagements as engineer, architect, CTO and has co-founded two product startups so far. At Hydrosphere.io, he is responsible for product vision, sales strategy, architecture and is still heavily involved in development.

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NEW DELHI: Accenture today said it has developed an artificial intelligence powered solution to help visually impaired people improve the way they experience the world around them and enhance their productivity in the workplace. The solution, called Drishti, was developed as a part of Accenture's focus on Tech4Good, which aims to apply technology to improve the way the world lives and works by solving complex social challenges, a company release said. Accenture, plans to introduce Drishti to more than 100 visually impaired employees in India. The solution is currently being piloted at Accenture in South Africa, and a Spanish language version is being tested with Accenture employees in Argentina. Drishti, which means'vision' in Sanskrit, provides smart phone-based assistance using AI technologies such as image recognition, natural language processing and natural language generation capabilities to describe the environment of a visually impaired person.

In this work, we design and evaluate a computational learning model that enables a human-robot team to co-develop joint strategies for performing novel tasks that require coordination. The joint strategies are learned through "perturbation training," a human team-training strategy that requires team members to practice variations of a given task to help their team generalize to new variants of that task. We formally define the problem of human-robot perturbation training and develop and evaluate the first end-to-end framework for such training, which incorporates a multi-agent transfer learning algorithm, human-robot co-learning framework and communication protocol. Our transfer learning algorithm, Adaptive Perturbation Training (AdaPT), is a hybrid of transfer and reinforcement learning techniques that learns quickly and robustly for new task variants. We empirically validate the benefits of AdaPT through comparison to other hybrid reinforcement and transfer learning techniques aimed at transferring knowledge from multiple source tasks to a single target task. We also demonstrate that AdaPT's rapid learning supports live interaction between a person and a robot, during which the human-robot team trains to achieve a high level of performance for new task variants. We augment AdaPT with a co-learning framework and a computational bi-directional communication protocol so that the robot can co-train with a person during live interaction. Results from large-scale human subject experiments (n=48) indicate that AdaPT enables an agent to learn in a manner compatible with a human's own learning process, and that a robot undergoing perturbation training with a human results in a high level of team performance. Finally, we demonstrate that human-robot training using AdaPT in a simulation environment produces effective performance for a team incorporating an embodied robot partner.

The problem of finding a minimum vertex cover (MinVC) in a graph is a well known NP-hard combinatorial optimization problem of great importance in theory and practice. Due to its NP-hardness, there has been much interest in developing heuristic algorithms for finding a small vertex cover in reasonable time. Previously, heuristic algorithms for MinVC have focused on solving graphs of relatively small size, and they are not suitable for solving massive graphs as they usually have high-complexity heuristics. This paper explores techniques for solving MinVC in very large scale real-world graphs, including a construction algorithm, a local search algorithm and a preprocessing algorithm. Both the construction and search algorithms are based on low-complexity heuristics, and we combine them to develop a heuristic algorithm for MinVC called FastVC. Experimental results on a broad range of real-world massive graphs show that, our algorithms are very fast and have better performance than previous heuristic algorithms for MinVC. We also develop a preprocessing algorithm to simplify graphs for MinVC algorithms. By applying the preprocessing algorithm to local search algorithms, we obtain two efficient MinVC solvers called NuMVC2+p and FastVC2+p, which show further improvement on the massive graphs.

Two-sample feature selection is the problem of finding features that describe a difference between two probability distributions, which is a ubiquitous problem in both scientific and engineering studies. However, existing methods have limited applicability because of their restrictive assumptions on data distributoins or computational difficulty. In this paper, we resolve these difficulties by formulating the problem as a sparsest $k$-subgraph problem. The proposed method is nonparametric and does not assume any specific parametric models on the data distributions. We show that the proposed method is computationally efficient and does not require any extra computation for model selection. Moreover, we prove that the proposed method provides a consistent estimator of features under mild conditions. Our experimental results show that the proposed method outperforms the current method with regard to both accuracy and computation time.

China's technology firms are rushing to apply the commercial use of facial recognition technology, bypassing the same privacy concerns that have slowed the roll out of the technology in Western markets, according to a report by The Financial Times. People in China are arguably less concerned about privacy rights violation than in Western countries as they are accustomed to having their faces scanned to conduct daily tasks, such as making payments to access residential blocks, student dormitories and hotels. In addition, Chinese citizens are required to swipe their ID cards into chip readers to activate a mobile phone account, purchase a train ticket or check into a hotel. Ant Financial, the online payments division of ecommerce group Alibaba, allows users to take a selfie to access their online wallets, while China Construction Bank offers a similar service for customers at ATMs. Car-hailing service Didi Chuxing is using the technology to verify drivers' identities, while search engine Baidu has developed facial recognition-enabled entry to access its offices and paid events.

We live in a world of chatbots. Chatbots improve human interaction with systems by giving a response based on the user input. This means chatbots are simple automated programs that can process simple user inputs and provide a meaningful output. But with time, chatbots evolved and now are impacting the industries around us. We live in a world of chatbots.